Enhancing clinical reliability in pressure injury prediction: A conformal prediction approach with machine learning
Fredy Barriga-Gallegos1, Gonzalo Ríos-Vásquez2, Hanns de la Fuente-Mella3
1Institute for Health Care Research, Faculty of Nursing, Universidad Andrés Bello, Santiago, Chile.
Background:
Pressure injuries (PIs) remain a major global healthcare challenge, increasing morbidity and costs. While Machine Learning (ML) models effectively predict PI risk, their lack of uncertainty quantification limits clinical trust. Conformal Prediction (CP) addresses this issue by providing statistically valid confidence estimates, enhancing model transparency and reliability in clinical practice.
Objective:
This study aims to improve the reliability and clinical applicability of PI prediction models by integrating CP with traditional ML algorithms, enabling uncertainty-aware predictions for safer and more transparent clinical decisions.
Methods:
A methodological study was conducted to evaluate ML classifiers for PI risk classification using routinely collected clinical data and alternative data-processing strategies. Model performance was assessed through paired statistical comparisons across resampling folds. Models showing superior performance were subsequently calibrated using a CP framework to generate uncertainty-aware prediction sets, which were evaluated using coverage and efficiency metrics across different confidence levels.
Results:
The overall incidence of PIs was 27%, with significant predictors including hospital ward, risk level, mattress use, containment, dermatitis, and age (p < 0.05). Among all models, XGBoost (XGB) and Random Forest (RF) showed the best predictive performance. After calibration with CP, both produced well-aligned uncertainty estimates. At α = 0.10, XGB achieved coverage = 0.949 and efficiency = 1.34, outperforming RF with tighter, more informative prediction sets, with a loss of 5% of cases outside the prediction set and an average size of the prediction set closer to 1.0 compared to the RF model. This framework enhanced model interpretability in clinical settings without compromising accuracy.
Conclusions:
Integrating CP into ML models may improve the interpretability and reliability of risk predictions by quantifying uncertainty. Although the findings are promising, they should be interpreted with caution given the modest sample size, event rate, single-center design, and potential variability in clinical documentation. This framework provides a foundation for uncertainty-aware decision support in PI prevention.